Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model

Abstract

Because of the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere–ocean–land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin-experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation, and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. The four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic-dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root-mean-square errors as 41%, 23%, 62%, and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature, and land surface temperature, respectively. Consistently, the new parameter optimization greatly improves the model predictability as a result of the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic-dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction.

Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

“…Furthermore, the 4D-Var method can also be used to optimize the model parameters, as demonstrated by the ensemble Kalman filter (Wu et al, 2012(Wu et al, , 2016. For example, the performance of the ICM is sensitive to α τ and α Te (Zhang et al, 2005a;; we plan to use 4D-Var to optimally determine these two parameters to further improve the ENSO prediction skill.…”

Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review

“…This is particularly important in the context of short-to medium-range weather forecasting because it would allow for a flexible and computationally efficient model optimization (Wu et al 2012).…”

Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model

“…Wu et al (2012) further introduced a geographic dependent parameter optimization (GPO) scheme to increase the signal-to-noise ratio of the background error covariance in parameter estimation, and examined the impact of this new scheme on climate estimation and prediction using an intermediate coupled model within a perfect model framework (Wu et al, 2013). Recently, Zhang et al (2013b) investigated the impact of parameter estimation on climate estimation and prediction in an intermediate coupled model with biased physics within a biased twin experiment framework, which indicates that the adverse impact of biased physical schemes in a coupled model on climate estimation and prediction can be compensated partly by optimizing the most sensitive parameters employed in the physical schemes.…”